Magnetic resonance imaging (“MRI”) is one of the most versatile and fastest growing modalities in medical imaging. As part of the MRI process, the subject patient is placed in an external magnetic field. This field is created by a magnet assembly, which can be closed or open. Open magnet assemblies have two spaced-apart magnet poles separated by a gap, and a working magnetic field volume located within the gap.
An MRI scanning procedure requires a coordinated effort among many pieces of hardware and software, and the diagnostic quality of images produced by MRI is directly related to several system performance characteristics. Collection of scan data and production of images from the scan data must be carried out with precision if the resulting images are to have diagnostic value. Glitches in the procedure, due to hardware fatigue, software malfunction, electrical noise, temperature effects, or any other deviation by any operational parameter or outside influence outside of an acceptable tolerance can have a negative effect on the results. For example, temporary EMI may cause the MRI receiver to pick up ‘spikes’ that have a very broad spectrum, which in turn will have a negative effect on the scan data and images. The MRI image data could suffer from artifacts, such as a degraded signal-to-noise ratio or extraneous lines or grids, sometimes to an extent that the original object is beyond recognition.
It would be advantageous to detect operational parameter deviations and other errors during the course of the scan procedure. However, this isn't always possible, and images often have artifacts or other deficiencies that reduce their diagnostic value, sometimes to the extent that the images are not usable, requiring a new scan.
The term K-space is commonly used with respect to MRI and other NMR applications. When working with MRI data, the observed signals often can be described in a much simpler way in terms of K-space, the linear vector space of n-dimensional complex vectors k based on a Fourier transform of the time-domain data, than using a strictly physical description based on ordinary Euclidean R-space, although distributions in K-space and R-space carry the same information.
After the initial excitation pulse of an MRI pulse sequence, it is possible to depart from the origin of the K-space and move along any desired K-space path using the gradients. Along this path, a record of the Q(k) values can be built for a subset of the visited K-space points. As different paths are collected, the signal becomes the value of a static function Q(k) at a set of visited points. Charting the function Q(k) at a set of points dense enough to makes it possible to carry out, with reasonable precision, the back-transformation into R-space.
As noted above, glitches in an MRI scan procedure due to any number of reasons can have a negative effect on the scan results, and therefore on the image. In such a case, some part of K-space would also be damaged. It would be advantageous to be able to detect and compensate for the errors that cause unusable images, such that these images can be corrected with little or no noticeable degradation in image quality.